Real-time traffic sign recognition based on a general purpose GPU and deep-learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Kwangyong | - |
dc.contributor.author | Hong, Yongwon | - |
dc.contributor.author | Choi, Yeongwoo | - |
dc.contributor.author | Byun, Hyeran | - |
dc.date.available | 2021-02-22T11:15:48Z | - |
dc.date.issued | 2017-03 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8630 | - |
dc.description.abstract | We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). | - |
dc.format.extent | 22 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.title | Real-time traffic sign recognition based on a general purpose GPU and deep-learning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1371/journal.pone.0173317 | - |
dc.identifier.scopusid | 2-s2.0-85014734268 | - |
dc.identifier.wosid | 000396054300051 | - |
dc.identifier.bibliographicCitation | PLOS ONE, v.12, no.3, pp 1 - 22 | - |
dc.citation.title | PLOS ONE | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 22 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.identifier.url | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173317 | - |
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